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doi: 10.1080/01431169508954604pmid: N/A
Abstract The present study relates to the climatologic component of the Global Change Program and uses Advanced Very High Resolution Radiometer (AVHRR) data. The analysis of the radiative transfer process between the surface and space shows that the Thermal Infra-Red Emsissivity (TIRE) can be expanded with respect to a non-dimensional small parameter related to the difference between atmospheric absorptions of AVHRR channels 11 and 12μm, respectively. The discussion shows that retrieved values of TIRE remain in good accordance with those expected. The land surface temperature can be expanded in a similar manner, with respect to the split window technique which indicates an inverse relation between temporal variables of the Normalized Difference Vegetation Index (NDVI) and TIRE.
GHITTER, G. S.; HALL, R. J.; FRANKLIN, S. E.
doi: 10.1080/01431169508954605pmid: N/A
Abstract In this study, we examine Landsat TM satellite multispectral imagery and several image processing strategies to determine the most accurate method to detect and map white spruce understories in deciduous and mixed-wood stands in Alberta. These stands may be considered as part of the conifer land base that is defined as stands which contain or are projected to contain a minimum conifer volume at rotation. Images acquired in late April (leaf-off) and late July (leaf-on) were used to generate signatures for three levels of understory (heavy, light, nil) in five overstory classes. Separability statistics indicate that a reasonable degree of success can be obtained in mapping some of the understory classes with conventional classification tools. Linear discriminant functions using different classification schema and discriminating variables are presented to indicate the level of accuracy that may be obtained in a supervised classification mapping exercise.
doi: 10.1080/01431169508954606pmid: N/A
Abstract Spectral data from blue to near-infrared (lR) were sampled at three different dates in 1992 from a fire damaged forest region near Berlin (Germany) and have been analysed by a principal component analysis, by the Normalized Difference Vegetation Index (NDVI) and by a self-organizing feature map (SOM) algorithm. The properties of SOMs are summarized and it is shown that the introduction of lateral network connections allows an easy clustering of the resulting topological feature space. The SOMs reveal interesting land surface features and suggest, with the clustering scheme applied. further work with this new type of classification algorithm.
PAOLA, J. D.; SCHOWENGERDT, R. A.
doi: 10.1080/01431169508954607pmid: N/A
Abstract A literature survey and analysis of the use of neural networks for the classification of remotely-sensed multi-spectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding, (2) output encoding and extraction of classes, (3) network architecture, (4) training algorithms, and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its nonparametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.
DAYA SAGAR, B.S.; VENU, M.; PRAKASA RAO, B. S.
doi: 10.1080/01431169508954608pmid: N/A
Abstract A large number of digitized surface water bodies are automatically distributed on the basis of size and shape by performing an opening transformation. In addition, an iteraled bisecting process is applied to construct self-similar size distribution of water bodies.
KUTSER, T.; ARST, H.; MILLER, T.; KÄÄRMANN, L.; MILIUS, A.
doi: 10.1080/01431169508954609pmid: N/A
Abstract The possibilities of solving the passive optical remote sensing inverse problem in the case of a turbid, multi-componental aquatic environment are considered using the data on Lake Peipsi. A well known method based on the correlative relations between the characteristics of the remotely-sensed radiance spectra and other characteristics of the water body is used. Telespectrometrical measurements above Lake Peipsi as well as the simultaneous underwater measurements were carried out during two helicopter and two ship expeditions. It was shown that using the remote sensing data obtained by means of a single measurement series it is possible to describe rather well the actual spatial distribution of the water mass characteristics on Lake Peipsi, but only for the expedition under consideration. However, not a single remote sensing characteristic of the twelve considered by us can give well matching isolines if we use the regression formulae determined on the basis of the whole data set (four expeditions together). Presumably universal algorithms describing the connections between the characteristics of the optical remote sensing spectra and the properties of the water mass in different water bodies and different weather conditions do not exist.
BRAUDE, C.; BEN YOSEF, N.; DaR, I.
doi: 10.1080/01431169508954610pmid: N/A
Abstract SPOT multi-spectral images of nearly 100 water reservoirs were analysed. The image analysis was supported by ground measurements which included water sampling and detailed laboratory analysis of the water constituents and concentration. The analysed reservoirs covered a wide range, from open drinking water reservoirs to polluted hypertrophic ones. Classification of reservoirs by their water quality is achieved by the study of the spectral distribution of the reflected solar radiation, taking into account the atmospheric effects. The techniques included chromatic coordinates analysis and principal component analysis. An optical model of the volume reflectance of the water body was constructed, based on the radiative transfer equation which includes the scattering properties of the main water constituents. Numerical simulations, based on the model, support the experimental findings regarding the reservoir c1assilication according to water quality and composition.
ZAGOLSKI, F.; GASTELLU-ETCHEGORRY, J.P.
doi: 10.1080/01431169508954611pmid: N/A
Abstract An algorithm for atmospheric correction was developed for correcting AVIRIS (Airborne Visible and Infrared Imaging Spectrometer) images that were acquired during the 1991 Mac Europe campaign ofNASA/JPL over the ‘Landes’ (south-west France). The methodology is based on the inversion of the 5S atmospheric model, through an iterative procedure that uses the Gauss Seidel principle. The environmental effect is fully taken into account, on a pixel per pixel basis, by the usc of circular neighbourhoods the radii of which are variable with wavelength. Here, input parameters, i.e. optical characteristics of the atmosphere. are estimated with ill situ atmospheric profile and visibility measurements combined with the 5S model. The visual analysis of AVIRIS spectral bands in the blue region clearly showed a heterogeneous spatial distribution of aerosol etTects. Consequently, a procedure was developed which computes the aerosol optical depth directly from the image. The only assumption is the presence of dense dark vegetation with spectral reflectances lying in narrow intervals the bounds of which, unknown at first, are iteratively determined. Spectral bands centred at 459·8 nm (band 7), 489·4 nm (band 10) and 607·9 nm (band 22) were the most efficient sensor's bands for that approach. The spatial variation of the aerosol optical depth was [0·16–0·26], [0·15–0·23] and [0·10–0·18] in the 459·8 nm, 489·4 nm and 607·9 nm bands, respectively, with a mean 1·4 ångstrom exponent. Spectral aerosol optical depth maps were computed and used as input parameters in the atmospheric correction procedure. This converged after five iterations, for all AVIRIS spectral bands. This correction procedure was conducted with radii of circular neighbourhood ranging from 0 to 100 pixels, which allowed us to compare this approach with those procedures that do not take into account adjacency etTect or assume that the latter can be derived from the pixel value. Moreover, these computations allowed us to determine the minimal sizes of the circular neighbourhoods, for each spectral band, thus ensuring a good approximation of the adjacency effect; e.g., for the 459·8 nm band, a radius of 600m (i.e., 30 pixels) was necessary for obtaining corrected reflectances with an accuracy of 0·5 per cent.
doi: 10.1080/01431169508954612pmid: N/A
Abstract The classification error matrix is frequently used for assessment of the quality of statistical areal estimates provided by remote sensing. The difference between the row and column marginals within individual categories of an error matrix, as the percentage of the total number of subjects, might be a useful measure of systematic differences between the two classifications. The tabulated value of an error matrix is, however, the result of a sampling procedure, and it is important to know whether the differences are significantly larger than expected due to randomness. Formulae for computation of appropriate test statistics are provided, and a numerical example is used to demonstrate the calculation and interpretation of the statistics.
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